Diagnostic test for predicting metastasis and recurrence in cutaneous melanoma

ABSTRACT

The invention as disclosed herein in encompasses a method for predicting the risk of metastasis of a primary cutaneous melanoma tumor, the method encompassing measuring the gene-expression levels of at least eight genes selected from a specific gene set in a sample taken from the primary cutaneous melanoma tumor; determining a gene-expression profile signature from the gene expression levels of the at least eight genes; comparing the gene-expression profile to the gene-expression profile of a predictive training set; and providing an indication as to whether the primary cutaneous melanoma tumor is a certain class of metastasis or treatment risk when the gene expression profile indicates that expression levels of at least eight genes are altered in a predictive manner as compared to the gene expression profile of the predictive training set.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/783,755, filed Mar. 14, 2013, the disclosure of which is explicitlyincorporated by reference herein in its entirety.

BACKGROUND OF THE INVENTION

Cutaneous melanoma (CM) is an aggressive form of cancer presenting over76,000 diagnosed cases in 2012.¹ CM tumors develop through a number ofdiscreet stages during the progression from a benign melanocytic nevusto a malignant metastatic tumor. Generally, benign nevi present as thin,pigmented lesions. After the acquisition of key genetic mutations andthe initiation of cytoarchitectural modifications leading to shallowinvasion of the skin, the lesions begin growing radially, a processreferred to as the radial growth phase. Upon escape from growth controlmediated by surrounding keratinocytes, stromal invasion to deeperregions of the dermis occurs, marking the progression to the verticalgrowth phase. The vertical growth phase, along with the geneticalterations that accompany this process, is thought to be the criticalstepping stone in the development of metastatic melanoma.

As is common in many other cancers, if CM is detected in the earlystages of tumor progression and appropriately treated, then a long-termmetastasis free and overall survival following diagnosis is likely forthe majority of patients.^(2,3) For example, subjects diagnosed with lowrisk, stage I CM tumors have a 5-year overall survival rate of 91-97%.³A number of histological factors are used to stage CM and are associatedwith prognosis. These factors include Breslow thickness, mitotic index,ulceration, and spread of disease from the primary tumor to sentineland/or regional lymph nodes.³⁻⁷ Tumor stage is determined based uponthese histopathological parameters using the well-known TNM (T=primarytumor, N=regional lymph nodes, M=distant metastases) system that definesstages 0-IV.³ The TNM staging system is highly accurate formetastasis-free survival for stage 0 melanomas (5-year survival of 99%),and stage IV melanomas (5-year survival <10%), in which distantmetastasis was detected at the time of primary diagnosis. Metastasis andshort-term survival has been documented for subjects with stage Idisease, with 5-10% of stage I tumors reporting metastatic activity. So,while the majority of patients with clinical stage I disease have a lowchance of metastasis risk disease some patients will develop metastaticdisease.

Prognosis for clinical stage II and stage III cases has poor accuracy asthere is a large range within each stage and a larger overlap betweenthe stages in 5-year survival rates. Under the current staging system,the 5-year survival rate for clinical stage II subjects is 53-82%, whilethe stage III 5-year survival is rate 22-68%.^(3,8) The distinguishinghallmark between clinical stage II and stage III tumors is the presenceof localized metastasis of CM cells in the sentinel lymph node (SLN).Patients with a positive SLN are clinical stage III. However, high falsenegative rates and disease recurrence are associated with histologicalanalysis of SLNs as evidenced by the wide ranges of metastatic freesurvival and overall survival in stage II and III patients.Immunohistochemical and genetic amplification techniques designed toimprove the common hematoxylin and eosin staining methods for detectionof regional disease have been developed but only provide marginalimprovements^(9,10). Biomarkers have been identified in SLN tissue, andanalyzed to improve the ability to recognize CM cells in SLNs, however,these methods have limited improvement in accuracy and are compounded byextensive tissue sampling from invasive biopsy of the lymph node.^(6,11)In addition, melanomas can enter the blood directly by intravasatinginto venous capillaries. Thus, the low sensitivity of SLN biopsy mayrelate to a direct hematogenous metastatic event versus an inaccurateSLN biopsy result. Inaccurate prognosis for metastatic risk has profoundeffects upon patients that are treated according to a populationapproach rather than an individual or personalized approach. Forexample, CM patients categorized as stage III through the use of currenthistological techniques, but who have an actual individual risk ofmetastasis that is low (false positive), are inappropriately exposed toover-treatment that includes enhanced surveillance, nodal surgery, andchemotherapy.¹² Similarly, patients determined to have stage I or IIdisease who actually have a high risk for metastasis (false negative),are at risk of under-treatment. In addition, SLN biopsy exposes patientsto significant clinical complications, such as lymphedema, and has a lowpositivity rate. For example, guidelines currently recommend thatpatients with CM staged at 1b (Breslow's thickness ≧0.75 mm but <1.00 mmor presence of ≧1 mitosis at any Breslow's thickness) are recommended toundergo SLN biopsy yet only 5% of SLN yield positivity. Meaning that of20 patients with stage 1b melanomas who undergo SLN biopsy, 19 will benegative and exposed to a surgical complication of SLN biopsy.¹²Similarly, all pathologic stage II patients (Breslow's thickness >1.0 mmare recommended to undergo SLN biopsy yet only 18% will have a positiveSLN.^(12,13)

To this end, gene expression profile (GEP) signatures have beendeveloped and some have been shown to have powerful prognosticcapabilities in a number of malignant diseases¹⁴⁻¹⁸. One such signaturehas been used for prognostication of uveal melanoma, a tumor ofmelanocytic origin that develops in the eye. Like cutaneous melanoma,treatment of the primary uveal tumor is highly effective. 2-4% of uvealmelanoma patients present with evidence of clinical metastasis at thetime of diagnosis, yet up to 50% of uveal melanoma patients developsystemic metastases within five years of diagnosis regardless of primaryeye tumor treatment (radiation therapy or enucleation)¹⁹. This meansthat a micrometastatic event has occurred in approximately 50% of uvealmelanoma patients prior to primary eye tumor treatment. A GEP signaturehas recently been developed that can accurately distinguish uvealmelanoma tumors that have a low risk of metastasis from those that havea high risk^(14,20). To assess genetic expression RT-PCR analysis isperformed for fifteen genes (twelve discriminating genes and threecontrol genes) that are differentially expressed in tumors with knownmetastatic activity compared to tumors with no evidence of metastasis.The uveal melanoma gene signature separates cases into a low risk groupthat has greater than 95% metastasis free survival five years afterdiagnosis, and a high risk group with less than 20% metastasis freesurvival at the same time point. The signature has been extensivelyvalidated in the clinical setting, and has been shown to provide asignificant improvement in prognostic accuracy compared toclassification by TNM staging criteria^(20,21).

A number of groups have published genomic analysis of tumors incutaneous melanoma²²⁻²⁹. While some studies have focused on the geneticalterations in malignant melanoma cells compared to normal melanocytes,others have compared benign nevi to tumors in the radial or verticalgrowth phases, or primary tumor to metastatic tumors. At the time thestudies contained within this patent were designed and implemented(2010), no evidence could be found in the literature or other publicdomain sources that indicated a gene expression profile test focusedsolely on the primary melanoma tumor could be developed for the clinicalapplication of predicting metastasis in patients with CM.^(22-24,27-29)In addition, all studies utilized fresh frozen CM samples rather thanformalin fixed paraffin embedded (FFPE) tumor tissue. All of the studiesrelated to this invention have only used FFPE primary tumor tissue.

SUMMARY OF THE INVENTION

There is a need in the art for a more objective method of predictingwhich tumors display aggressive metastatic activity. Development of anaccurate molecular footprint, such as the gene expression profile assayencompassed by the invention disclosed herein, by which CM metastaticrisk could be assessed from primary tumor tissue would be a significantadvance forward for the field.

In one embodiment, the invention as disclosed herein is a method forpredicting the risk of metastasis of a primary cutaneous melanoma tumor,the method comprising: (a) measuring the gene-expression levels of atleast eight genes selected from the group consisting of BAP1_varA,BAP1_varB, MGP, SPP1, CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B,GJA, ID2, EIF1B, S100A9, CRABP2, KRT14, ROBO1, RBM23, TACSTD2, DSC1,SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H, and CST6, in a sample takenfrom the primary cutaneous melanoma tumor, wherein measuringgene-expression levels of the at least eight genes comprises measurementof a level of fluorescence by a sequence detection system followingRT-PCR; (b) determining a gene-expression profile signature comprisingthe gene expression levels of the at least eight genes; (c) comparingthe gene-expression profile to the gene-expression profile of apredictive training set; and (d) providing an indication as to whetherthe primary cutaneous melanoma tumor is class 1 or class 2 of metastasiswhen the gene expression profile indicates that expression levels of atleast eight genes are altered in a predictive manner as compared to thegene expression profile of the predictive training set.

In another embodiment, the invention as disclosed herein is a method forpredicting the risk of metastasis of a primary cutaneous melanoma tumor,the method comprising: (a) measuring the gene-expression levels of atleast eight genes selected from the group consisting of BAP1_varA,BAP1_varB, MGP, SPP1, CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B,GJA, ID2, EIF1B, S100A9, CRABP2, KRT14, ROBO1, RBM23, TACSTD2, DSC1,SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H, and CST6, in a sample takenfrom the primary cutaneous melanoma tumor, wherein measuringgene-expression levels of the at least eight genes comprises measurementof a level of fluorescence by a sequence detection system followingRT-PCR; (b) determining a gene-expression profile signature comprisingthe gene expression levels of the at least eight genes; (c) comparingthe gene-expression profile to the gene-expression profile of apredictive training set; and (d) providing an indication as to whetherthe primary cutaneous melanoma tumor is class A, class B, or class C ofmetastasis when the gene expression profile indicates that expressionlevels of at least eight genes are altered in a predictive manner ascompared to the gene expression profile of the predictive training set.In one embodiment, the invention as disclosed herein is a method forpredicting the risk of metastasis of a primary cutaneous melanoma tumor,the method comprising: (a) measuring the gene-expression levels of atleast eight genes selected from the group consisting of BAP1_varA,BAP1_varB, MGP, SPP1, CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B,GJA, ID2, EIF1B, S100A9, CRABP2, KRT14, ROBO1, RBM23, TACSTD2, DSC1,SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H, and CST6, in a sample takenfrom the primary cutaneous melanoma tumor, wherein measuringgene-expression levels of the at least eight genes comprises measurementof a level of fluorescence by a sequence detection system followingRT-PCR; (b) determining a gene-expression profile signature comprisingthe gene expression levels of the at least eight genes; (c) comparingthe gene-expression profile to the gene-expression profile of apredictive training set; and (d) providing an indication as to whetherthe primary cutaneous melanoma tumor has a low risk to high ofmetastasis when the gene expression profile indicates that expressionlevels of at least eight genes are altered in a predictive manner ascompared to the gene expression profile of the predictive training set.

In an additional embodiment, the invention as disclosed herein is amethod of treating cutaneous melanoma in a patient, comprising the stepsof: (a) obtaining the gene expression level of at least one of BAP1,MGP, SPP1, CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B, GJA, ID2,EIF1B, S100A9, CRABP2, KRT14, ROBO1, RBM23, TACSTD2, DSC1, SPRR1B,TRIM29, AQP3, TYRP1, PPL, LTA4H, and CST6 in a sample taken from theprimary cutaneous melanoma tumor, wherein measuring gene-expressionlevels of the at least eight genes comprises measurement of a level offluorescence by a sequence detection system following RT-PCR; (b)comparing the gene expression level of the at least one gene to thegene-expression level the same gene or genes taken from a predictivetraining set; (c) making a determination as to whether thegene-expression level of the at least one gene is altered in apredictive manner; and (d) targeting the at least one gene for therapywhen the determination is made in the affirmative.

Specific embodiments of the invention will become evident from thefollowing more detailed description of certain embodiments and theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts statistical analysis and graphs illustrating Kaplan-Meier(K-M) analysis of metastasis free survival (MFS) for CM cases predictedto be at low-risk (class 1) or high-risk (class 2) of metastasisaccording to a radial basis machine (RBM) modeling algorithm. 5-yr MFSfor the 162 sample training set (A) is 93% for class 1 cases and 31% forclass 2 cases (p<0.0001), while the entire cohort has a 5-yr MFS rate of64%. 5-yr MFS for the independent 111 sample validation set (B) is 97%for class 1 compared to 33% for class 2 (p<0.0001), with a 69% rate inthe combined cohort. Accuracy of the training set model, as measured byROC, is 0.8730, compared to 0.9130 for the validation cohort, eachreflecting clinically valuable models.

FIG. 2 depicts statistical and K-M analysis of the 162 sample trainingset when metastasis is predicted using partition tree (A), K-nearestneighbor (B), logistic regression (C), or discriminant analysis (D)modeling algorithms. Highly significant differences in K-M MFS curvesfor class 1 and class 2 cohorts are observed with each modeling method.Accuracy is highest when analysis is performed with the partition treemodel (ROC=0.9901; accuracy=92%; sensitivity=96%), while the othermodels are also statistically accurate, with ROC >0.8.

FIG. 3 depicts the same statistics and graphs as FIG. 2, but withpartition tree (A), K-nearest neighbor (B), logistic regression (C), ordiscriminant analysis (D) used to analyze the independent 111 samplevalidation set. Significant differences between class 1 and class 2 K-Mcurves are observed using each modeling method. Algorithm accuracy ishighest for the K-nearest neighbor model (ROC=0.8935), while the highestsensitivity, or accuracy of predicting class 2 high risk cases, wasobserved when using the partition tree model (sensitivity=94%). 4

FIG. 4 depicts statistics and K-M analysis reflecting the impact ofstage 0 in situ melanomas on training set predictive power. Removingstage 0 cases from the original training and validation cohorts producesa 149 sample training set (A) and independent 107 sample validation set(C). Significant differences are observed in class 1 and class 2 K-Mcurves for both cohorts (p<0.0001), and metastatic risk prediction isaccurate in both the training and validation sets (ROC=0.8394 and0.9061, respectively). Analysis was also performed following inclusionof all stage 0 samples in the training set, producing a training cohortof 166 samples (B) that exhibited better accuracy (ROC=0.8846;Accuracy=83%; sensitivity=89%) compared to the 149 sample training set.No dramatic differences are observed when the independent 107 samplevalidation set is trained using the 166 sample cohort (D) compared to(C).

FIG. 5 depicts MFS statistics and accuracy of the genetic signature whenused to predict metastatic risk in superficial spreading (A) or nodular(B) type CM tumors. K-M MFS was 100% for superficial spreading casespredicted to be class 1, and only 5% for cases predicted to be class 2(p<0.0001). The predictive model had an accuracy of 100% for predictingboth classes, and ROC=1.00. Prediction of risk for nodular tumors wasless accurate, with an ROC=0.9323, accuracy of 82%, and sensitivity of81%, but K-M analysis reflects a significant difference between the 81%5-year MFS for Class 1 and the 7% MFS for Class 2 cases (p<0.0001).

FIG. 6 depicts MFS graphs and statistical analysis following a tri-modalprediction for CM cases at low-risk (class A), intermediate risk (classB) or high-risk (class C) of metastasis according to a radial basismachine (RBM) modeling algorithm. 5-yr MFS for the independent 107sample validation set is 98% for class A compared to 94% for class B and30% for class C (p<0.0001), with a 64% rate in the overall cohort,reflecting clinically valuable models.

FIG. 7 depicts MFS for the 107 sample validation cohort based upon theprobability score generated for each case during predictive modelinganalysis. Subgroups A-J represent 0.1 unit incremental increases in theprobability of metastasis (0=low risk of metastasis, 1=high risk ofmetastasis), as determined by the RBM algorithm. Importantly, increasesin probability score correspond to decreases in MFS rates. For example,all cases in subgroup A have a probability score between 0 and 0.099,and there is 100% 5 year MFS in the cohort. Conversely, each of thecases is subgroup J has a probability score between 0.9 and 1.0, andnone of those cases is metastasis free at the 5 year time point.Statistically significant differences are observed between the subgroups(p<0.0001), and 5-year MFS is shown in the figure legend.

DETAILED DESCRIPTION OF THE INVENTION

The inventors reviewed the current state of the art for evidence ofmicroarray analyses focused on distinguishing differential geneticexpression profiles that characterize cutaneous melanoma tumors. Sevenstudies were identified that utilized microarray technology to determinegenetic expression comparing various stages of cutaneousmelanoma.^(22-27,29) The objective was to identify genes that weredysregulated between radial growth phase to vertical growth phase, or inprimary melanomas compared to metastatic tumors. Mauerer, et. al.,assessed gene expression differences in melanocytic nevi compared toprimary melanoma, melanocytic nevi compared to metastatic melanoma, andprimary melanoma compared to metastatic melanoma²⁵. Nothing in the artwas found relating to evaluation of primary cutaneous melanoma tumorsrelative to subsequent metastatic vs. non-metastatic outcomes. In anattempt to arrive at a putative gene expression profile set, theinventors focused on genes isolated from primary melanoma tumor samplesand metastatic melanoma tumor samples that had an observed up-regulationgreater than 2-fold, or down-regulation greater than 3-fold, andestablished those as potential mediators of metastatic progression. Bythese criteria, 26 up-regulated genes and 78 down-regulated genes werechosen as the basis for comparison to other expression analysis studies.This 104-gene panel was subsequently compared to expression data setsreported in Scatolini et. al., Jaeger, et. al., Winnipenninckx et. al.,Haqq, et. al., Smith et. al., and Bittner et. al.^(22-24,26,27,29)Additionally, the expression data from Onken, et. al., which reported 74genes that were differentially regulated in metastatic andnon-metastatic uveal melanoma tumors was compared to the 104-genepanel.¹⁴

In one embodiment, the invention as disclosed herein is a method forpredicting the risk of metastasis of a primary cutaneous melanoma tumor,the method comprising: (a) measuring the gene-expression levels of atleast eight genes selected from the group consisting of BAP1_var_A,BAP1_var_B, MGP, SPP1, CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B,GJA, ID2, EIF1B, S100A9, CRABP2, KRT14, ROBO1, RBM23, TACSTD2, DSC1,SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H, and CST6, in a sample takenfrom the primary cutaneous melanoma tumor, wherein measuringgene-expression levels of the at least eight genes comprises measurementof a level of fluorescence by a sequence detection system followingRT-PCR; (b) determining a gene-expression profile signature comprisingthe gene expression levels of the at least eight genes; (c) comparingthe gene-expression profile to the gene-expression profile of apredictive training set; and (d) providing an indication as to whetherthe primary cutaneous melanoma tumor is class 1 (low risk) or class 2(high risk) of metastasis when the gene expression profile indicatesthat expression levels of at least eight genes are altered in apredictive manner as compared to the gene expression profile of thepredictive training set.

In another embodiment, the invention as disclosed herein is a method oftreating cutaneous melanoma in a patient, comprising the steps of: (a)obtaining the gene expression level of at least one of BAP1_varA,BAP1_varB, MGP, SPP1, CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B,GJA, ID2, EIF1B, S100A9, CRABP2, KRT14, ROBO1, RBM23, TACSTD2, DSC1,SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H, and CST6 in a sample taken fromthe primary cutaneous melanoma tumor, wherein measuring gene-expressionlevels of the at least eight genes comprises measurement of a level offluorescence by a sequence detection system following RT-PCR; (b)comparing the gene expression level of the at least one gene to thegene-expression level the same gene or genes taken from a predictivetraining set; (c) making a determination as to whether thegene-expression level of the at least one gene is altered in apredictive manner; and (d) targeting the at least one gene for therapywhen the determination is made in the affirmative.

In an additional embodiment, the invention as disclosed herein is amethod for predicting the risk of metastasis of a primary cutaneousmelanoma tumor, the method comprising: (a) measuring the gene-expressionlevels of at least eight genes selected from the group consisting ofBAP1_varA, BAP1_varB, MGP, SPP1, CXCL14, CLCA2, S100A8, BTG1, SAP130,ARG1, KRT6B, GJA, ID2, EIF1B, S100A9, CRABP2, KRT14, ROBO1, RBM23,TACSTD2, DSC1, SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H, and CST6, in asample taken from the primary cutaneous melanoma tumor, whereinmeasuring gene-expression levels of the at least eight genes comprisesmeasurement of a level of fluorescence by a sequence detection systemfollowing RT-PCR; (b) determining a gene-expression profile signaturecomprising the gene expression levels of the at least eight genes; (c)comparing the gene-expression profile to the gene-expression profile ofa predictive training set; and (d) providing an indication as to whetherthe primary cutaneous melanoma tumor is class A (low risk), class B(intermediate risk), or class C (high risk) of metastasis when the geneexpression profile indicates that expression levels of at least eightgenes are altered in a predictive manner as compared to the geneexpression profile of the predictive training set.

In one embodiment, the invention as disclosed herein is a method forpredicting the risk of metastasis of a primary cutaneous melanoma tumor,the method comprising: (a) measuring the gene-expression levels of atleast eight genes selected from the group consisting of BAP1_varA,BAP1_varB, MGP, SPP1, CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B,GJA, ID2, EIF1B, S100A9, CRABP2, KRT14, ROBO1, RBM23, TACSTD2, DSC1,SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H, and CST6, in a sample takenfrom the primary cutaneous melanoma tumor, wherein measuringgene-expression levels of the at least eight genes comprises measurementof a level of fluorescence by a sequence detection system followingRT-PCR; (b) determining a gene-expression profile signature comprisingthe gene expression levels of the at least eight genes; (c) comparingthe gene-expression profile to the gene-expression profile of apredictive training set; and (d) providing an indication as to whetherthe primary cutaneous melanoma tumor has a low risk to high ofmetastasis when the gene expression profile indicates that expressionlevels of at least eight genes are altered in a predictive manner ascompared to the gene expression profile of the predictive training set.

As used here in, “metastasis” is defined as the recurrence or diseaseprogression that may occur locally (such as local recurrence and intransit disease), regionally (such as nodal micrometastasis ormacrometastasis), or distally (such as brain, lung and other tissues).“Class 1 or class 2 of metastasis” as defined herein includes low-risk(class 1) or high-risk (class 2) of metastasis according to any of thestatistical methods disclosed herein, Additionally, “cutaneous melanomametastasis” as used herein includes sentinel lymph node metastasis, intransit metastasis, distant metastasis, and local recurrence.

As used herein, a “sequence detection system” is any computationalmethod in the art that can be used to analyze the results of a PCRreaction. One example, inter alia, is the Applied Biosystems HT7900 fastReal-Time PCR system. In certain embodiments, gene expression can beanalyzed using, e.g., direct DNA expression in microarray, Sangersequencing analysis, Northern blot, the Nanostring® technology, serialanalysis of gene expression (SAGE), RNA-seq, tissue microarray, orprotein expression with immunohistochemistry or western blot technique.

As defined herein, “gene-expression profile signature” is anycombination of genes, the measured messenger RNA transcript expressionlevels or direct DNA expression levels or immunohistochemistry levels ofwhich can be used to distinguish between two biologically differentcorporal tissues and/or cells and/or cellular changes. In certainembodiments, gene-expression profile signature is comprised of thegene-expression levels of at least 28, 27, 26, 25, 24, 23, 22, 21, 20,19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, or 8 genes. In a furtherembodiments, the genes selected are KRT6B, GJA1, AQP3, TRIM29, TYRP1,RBM23, MGP and EIF1B; SAP130, ARG1, KRT6B, EIF1B, S100A9, KRT14, ROBO1,RBM23, TRIM29, AQP3, TYRP1 and CST6; GJA1, PPL, ROBO1, MGP, TRIM29,AQP3, RBM23, TACSTD2, TYRP1, KRT6B, EIF1B and DSC1; CRABP2, TYRP1, PPL,EIF1B, SPRR1B, DSC1, GJA1, AQP3, MGP, RBM23, CLCA2 and TRIM29; RBM23,TACSTD2, CRABP2, PPL, GJA1, SPP1, CXCL14, EIF1B, AQP3, MGP, LTA4H andKRT6B; S100A8, TACSTD2, BAP1_varA, KRT6B, EIF1B, TRIM29, TYRP1, CST6,PPL, RBM23, AQP3, GJA1, SPRR1B and ARG1; CST6, KRT6B, LTA4H, CLCA2,CRABP2, TRIM29, CXCL14, PPL, ARG1, RBM23, GJA1, AQP3, TYRP1, SPP1, DSC1,TACSTD2, EIF1B, and BAP1_varB.

As defined herein, “predictive training set” means a cohort of CM tumorswith known clinical metastatic outcome and known genetic expressionprofile, used to define/establish all other CM tumors, based upon thegenetic expression profile of each, as a low-risk, class 1 tumor type ora high-risk, class 2 tumor type. Additionally, included in thepredictive training set is the definition of “threshold points” pointsat which a classification of metastatic risk is determined, specific toeach individual gene expression level.

As defined herein, “altered in a predictive manner” means changes ingenetic expression profile that predict metastatic risk.

In one embodiment, the cutaneous melanoma tumor is taken from aformalin-fixed, paraffin embedded wide local excision sample. In anotherembodiment, the cutaneous melanoma tumor is taken from formalin-fixed,paraffin embedded punch biopsy sample.

In certain embodiments, analysis of genetic expression and determinationof outcome is carried out using radial basis machine and/or partitiontree analysis, LRA, K-nearest neighbor, or other algorithmic approach.These analysis techniques take into account the large number of samplesrequired to generate a training set that will enable accurate predictionof outcomes as a result of cut-points established with an in-processtraining set or cut-points defined for non-algorithmic analysis, butthat any number of linear and nonlinear approaches can produce astatistically significant and clinically significant result. Among theadvantages of use of the methods disclosed herein are relating to, e.g.,the in excess of 140 samples in the training set used to cover eitherheterogeneity or adequately handle smaller gene expression profilechanges that could not adequately predict outcomes in an independenttest set. As defined herein, “Kaplan-Meier survival analysis” isunderstood in the art to be also known as the product limit estimator,which is used to estimate the survival function from lifetime data. Inmedical research, it is often used to measure the fraction of patientsliving for a certain amount of time after treatment. “JMP Genomics®software” provides an interface for utilizing each of the predictivemodeling methods disclosed herein, and should not limit the claims tomethods performed only with JMP Genomics® software.

EXAMPLES

The Examples that follow are illustrative of specific embodiments of theinvention, and various uses thereof. They are set forth for explanatorypurposes only, and should not be construed as limiting the scope of theinvention in any way.

Materials and Methods 1. Cutaneous Melanoma Tumor Sample Preparation andRNA Isolation

Formalin fixed paraffin embedded (FFPE) primary cutaneous melanoma tumorspecimens arranged in 5 μm sections on microscope slides were acquiredfrom multiple institutions under Institutional Review Board (IRB)approved protocols. All tissue was reviewed by a pathologist andsections with <60% tumor in an area compatible with dissection wereexcluded from the study. Tissue with >60% tumor were marked and tumortissue was dissected from the slide using a sterile disposable scalpel,collected into a microcentrifuge tube, and deparaffinized using xylene.RNA was isolated from each specimen using the Ambion RecoverAll TotalNucleic Acid Isolation Kit (Life Technologies Corporation, Grand Island,N.Y.). RNA quantity and quality were assessed using the NanoDrop 1000system and the Agilent Bioanalyzer 2100.

2. cDNA Generation and RT-PCR Analysis

RNA isolated from FFPE samples was converted to cDNA using the AppliedBiosystems High Capacity cDNA Reverse Transcription Kit (LifeTechnologies Corporation, Grand Island, N.Y.). Prior to performing theRT-PCR assay each cDNA sample underwent a 14-cycle pre-amplificationstep. Pre-amplified cDNA samples were diluted 20-fold in TE buffer. 50ul of each diluted sample was mixed with 50 ul of 2× TaqMan GeneExpression Master Mix, and the solution was loaded to a custom highthroughput microfluidics gene card containing primers specific for 28class discriminating genes and 3 endogenous control genes. Each samplewas run in triplicate. The gene expression profile test was performed onan Applied Biosystems HT7900 machine (Life Technologies Corporation,Grand Island, N.Y.).

3. Expression Analysis and Class Assignment

Mean C_(t) values were calculated for triplicate sample sets, and ΔC_(t)values were calculated by subtracting the mean C_(t) of eachdiscriminating gene from the geometric mean of the mean C_(t) values ofall three endogenous control genes. ΔC_(t) values were standardizedaccording to the mean of the expression of all discriminant genes with ascale equivalent to the standard deviation. Three control genes wereselected based upon analysis using geNorm. Various linear and non-linearpredictive modeling methods, including radial basis machine, k-nearestneighbor, partition tree, logistic regression, discriminant analysis anddistance scoring, were performed using JMP Genomics SAS-based software(JMP, Cary, N.C.). Kaplan-Meier curves reflecting metastasis freesurvival were also generated in IMP, and statistical significance wascalculated according to the Log Rank method. Cox univariate andmultivariate regression analysis was performed using WinSTAT forMicrosoft Excel version 2012.1.

Example 1 Cutaneous Melanoma Metastatic Risk Genetic Signature andBiomarker Expression

Genetic expression of the discriminant genes in the signature (Table 1)was assessed in a cohort of 273 cutaneous melanoma samples using RT-PCR(FIG. 1). As shown in Table 2 below, of the 28 discriminating genes, 26were significantly altered in metastatic melanoma tumors compared tononmetastatic tumors (p<0.05, range 0.0366-6.08E-16), and 25 weredown-regulated. Genes that were up-regulated in the metastatic tumorsincluded SPP1, KRT6B, and EIF1B.

TABLE 1 Genes included in the GEP signature able to predict metastaticrisk from primary CM tumors. Gene Symbol Gene Title Gene Title SynonymsBAP1_varA BRCA1 associated protein-1 TPDS, UCHL2, HUCEP-13, hucep-6,BAP1 (a1), BAP1_var1 MGP Matrix Gla protein GIG36, MGLAP, NTI SPP1secreted phosphoprotein 1 PSEC0156, BNSP, BSPI, ETA-1, OPN CXCL14chemokine (C-X-C motif) ligand 14 UNQ240/PRO273, BMAC, BRAK, KEC, KS1,MIP-2g, MIP2G, NJAC, SCYB14 BAP1_varB BRCA1 associated protein-1 TPDS,UCHL2, HUCEP-13, hucep-6; BAP1 (a2), BAP1_var2 CLCA2 chloride channelaccesory 2 CACC, CACC3, CLCRG2, CaCC-3 S100A8 S100 calcium bindingprotein A8 60B8AG, CAGA, CFAG, CGLA, CP-10, L1Ag, MA387, MIF, MRP8, NIF,P8 BTG1 B-cell translocation gene 1, anti-proliferative SAP130Sin3A-associated protein, 130 kDa ARG1 arginase, liver KRT6B keratin 6BCK-6B, CK6B, K6B, KRTL1, PC2 GJA1 gap junction protein, alpha 1AU042049, AW546267, Cnx43, Cx43, Cx43alpha1, Gja-1, Npm1, connexin43 ID2inhibitor of DNA binding 2 AI255428, C78922, Idb2, bHLHb26 EIF1Beukaryotic translation initiation GC20 factor 1B S100A9 S100 calciumbinding protein A9 60B8AG, CAGB, CFAG, CGLB, L1AG, LIAG, MAC387, MIF,MRP14, NIF, P14 CRABP2 cellular retinoic acid binding protein 2RP11-66D17.4, CRABP-II, RBP6 KRT14 keratin 14 CK14, EBS3, EBS4, K14, NFJROBO1 roundabout, axon guidance receptor, DUTT1, SAX3 homolog 1(Drosophila) RBM23 RNA binding motif protein 23 PP239, CAPERbeta, RNPC4TACSTD2 tumor-associated calcium signal EGP-1, EGP1, GA733-1, GA7331,GP50, transducer 2 M1S1, TROP2 DSC1 desmocollin 1 CDHF1, DG2/DG3 SPRR1Bsmall proline-rich protein 1B CORNIFIN, GADD33, SPRR1 TRIM29 tripartitemotif containing 29 ATDC AQP3 aquaporin 3 AQP-3, GIL TYRP1tyrosinase-related protein 1 RP11-3L8.1, CAS2, CATB, GP75, OCA3, TRP,TRP1, TYRP, b-PROTEIN PPL periplakin LTA4H leukotriene A4 hydrolase CST6cystatin E/M

TABLE 2 Genes included in the GEP signature able to predict metastaticrisk from primary CM tumors. ΔCt value expression nonmeta- meta- changein direction of Gene static static metastatic expression symbol samplessamples samples p-value change BAP1_varA −1.290 −1.677 −0.388 0.007118down MGP −1.996 −2.190 −0.194 0.48585  down SPP1 −1.011 2.224 3.2356.08E−16 up CXCL14 3.021 0.828 −2.193 3.31E−12 down BAP1_varB 0.3810.003 −0.378 0.004646 down CLCA2 −3.468 −5.603 −2.135 1.02E−08 downS100A8 −0.450 −1.179 −0.728 0.030655 down BTG1 −2.422 −3.008 −0.5860.023606 down SAP130 −1.075 −1.405 −0.329 0.023626 down ARG1 −1.645−4.393 −2.749 1.05E−08 down KRT6B −1.809 −1.222 0.586 0.160458 up GJA1−2.882 −3.652 −0.770 0.034149 down ID2 −0.649 −1.411 −0.762 3.91E−06down EIF1B 0.041 0.350 0.309 0.023747 up S100A9 3.374 2.527 −0.8470.012385 down CRABP2 −0.087 −0.953 −0.866 0.00059  down KRT14 5.6543.927 −1.727 1.75E−05 down ROBO1 0.100 −0.364 −0.464 0.000406 down RBM23−2.788 −3.161 −0.374 0.018025 down TACSTD2 −3.485 −3.984 −0.499 0.03658 down DSC1 −0.102 −2.963 −2.861   7E−09 down SPRR1B 4.622 3.139 −1.4820.001392 down TRIM29 0.228 −2.239 −2.467 2.34E−09 down AQP3 3.413 1.848−1.565 5.08E−06 down TYRP1 1.276 −0.850 −2.125 2.41E−06 down PPL −0.082−2.233 −2.150 5.59E−11 down LTA4H −0.736 −1.275 −0.539 0.000156 downCST6 −0.535 −3.099 −2.563 1.02E−08 down

Example 2 Initial Training Set Development Studies and Comparison toValidation Cohort

Using JMP Genomics® software and clinical data analysis, a training setof 162 cutaneous melanoma samples was generated that could accuratelypredict the risk of metastasis based upon the 28 gene signature. Thetraining set contained 13 stage 0 in situ melanomas, 61 stage I, 70stage II, 17 stage III, and 1 stage IV melanomas. Metastatic risk wasassessed using a radial basis machine predictive modeling algorithm,which reports class 1 (low risk of metastasis) or class 2 (high risk ofmetastasis). ΔCt values generated from RT-PCR analysis of the trainingset cohort were standardized to the mean for each gene, with a scaleequivalent to the standard deviation. Analyses were also performed usingKNN, PTA, and discriminant analysis to confirm the results from the RBMapproach (as discussed below). The training set prediction algorithm wasthen validated using an independent cohort of 111 cutaneous melanomasamples, comprised of four stage 0 in situ melanomas, 56 stage I, 36stage II, 12 stage III, and 2 stage IV melanomas. Samples in thevalidation set were standardized prior to analysis using the factorialsgenerated during standardization of the training set. Area under thereceiver operator characteristic (ROC) curve, accuracy, sensitivity(prediction of high risk metastatic event), and specificity (predictionof nonmetastatic outcome) were statistical endpoints for the analysis.In the training set cohort, ROC=0.8730, accuracy=82%, sensitivity=89%,and specificity=74% (FIG. 1A). In the validation cohort, ROC=0.9130,accuracy=87%, sensitivity=94%, and specificity=79% (FIG. 1B).

Kaplan-Meier survival analysis was also performed for the training andvalidation cohorts. In the training set, 5-year metastasis free survival(MFS) was 93% for class 1 cases, and 31% for class 2 cases (p<0.0001;FIG. 1A). By comparison, 5-year MFS for the validation cohort was 97%for class 1 cases and 33% for class 2 cases (p<0.0001; FIG. 1B).Overall, the 5-year MFS rate for the entire training set, combiningclass 1 and class 2 cases, was 64%, while the combined MFS rate for thevalidation set was 69%.

Example 3 Analysis of 162 Sample Training Set with Multiple PredictiveModeling Methods

JMP® Genomics software allows for analysis using linear and non-linearpredictive modeling methods. To assess whether accuracy of metastaticrisk prediction for the validation cohort was limited to the RBM method,partition tree, K-nearest neighbor, logistic regression, anddiscriminant analysis were performed (FIGS. 2 and 3). Training set ROC,accuracy, sensitivity, specificity and K-M 5-year MFS for class 1 andclass 2 cases were highly comparable to the RBM method. Highly accurateprediction of metastasis, and significantly different 5-year MFS betweenclass 1 and class 2 cases was observed when using partition tree (FIG.2A), K-nearest neighbor (FIG. 2B), logistic regression (FIG. 2C), ordiscriminant analysis (FIG. 2D). Significant differences in 5-year MFSwere also observed for validation cohort class 1 and class 2 cases usingpartition tree (FIG. 3A), K-nearest neighbor (FIG. 3B), logisticregression (FIG. 3C), and discriminant analysis (FIG. 3D). Importantly,though, accuracy of prediction for validation samples was above 80% withall methods except discriminant analysis (FIG. 3D), and sensitivity, oraccuracy of prediction for cases with documented metastatic events, wasas high as 94% when using partition tree analysis.

Example 4 Evaluation of In Situ Melanoma Effect Upon Training SetAccuracy

To assess the impact of stage 0 in situ melanoma samples on thepredictive capabilities of the training set, either a) samples wereremoved from all stage 0 the training and validation cohorts, generatinga new training set of 149 cases; or b) all stage 0 samples were includedin the training set only, generating a training set comprised of 166cases. The new cutaneous melanoma predictive training sets were used totrain a 107 sample validation cohort (the 111 sample validation settested to this point, with four stage 0 in situs removed). Radial basismachine prediction was performed as described above, and ROC, accuracyof prediction, and 5-year MFS were assessed for the training andvalidation sets (FIG. 4). Training set statistics for both the 149sample and 166 sample training sets (FIGS. 4A and 4B) were highlycomparable to the 162 sample training set. ROC was highest for the 166sample set, but accuracy, sensitivity and specificity were identical inthe 162 and 166 sample training sets. K-M analysis yielded highlysignificant differences between class 1 and class 2 MFS with eachtraining set. Metastatic risk of the 107 samples in the validationcohort was accurately predicted with the 149 and 166 sample trainingsets (FIGS. 4C and 4D). Similar to the results seen for the validationset trained with the 162 sample cohort, both validation sets had ROCgreater than 0.9, reflecting highly relevant clinical models ofprediction, and sensitivity of 94%. Again, MFS was significantlydifferent between class 1 and class 2 regardless of the training setcohort used to predict risk in the validation set.

Example 5 Identification of Reduced Discriminant Gene Signatures withPredictive Accuracy

Accurate prediction of high risk metastatic cases is extremely importantin order to prevent those patients who are likely to metastasize fromreceiving a low risk treatment protocol. Thus, a measure of success forthe predictive gene set is achievement of greater than 88% sensitivityin both the training and validation sets. As shown in Table 3 below, andin FIG. 4, sensitivity is 89% for the 166 sample training set and 94%for the 107 sample validation set when the 28-gene signatureincorporating all discriminant genes is used to predict metastatic risk.Smaller sets of genes were generated that had equal or increasedsensitivity compared to the 28-gene signature (Table 3, bolded).However, the majority of gene sets that did not include all 28 geneswere not able to produce the sensitivity thresholds required for use ina clinically feasible GEP test.

TABLE 3 Sensitivity, or accuracy of predicting a metastatic event,achieved when using the 28-gene signature or smaller subsets of genes.Sensitivity # of training/validation Gene set variables sets (%) alldiscriminant genes 28 89/94 SPP1, CXCL14, BAP1_varA, CLCA2, S100A8, BTG16 79/89 TACSTD2, RBM23, PPL, S100A8, MGP, TYRP1 6 86/78 SAP130, ARG1,KRT6B, GJA1, EIF1B, ID2 6 81/81 CRABP2, KRT14, ROBO1, RBM23, TACSTD2,DSC1 6 70/81 ROBO1, CST6, BAP1_varB, ID2, SPRR1B, KRT6B 6 75/83 SPRR1B,AQP3, PPL, DSC1, TYRP1, TRIM29 6 81/81 KRT6B, GJA1, AQP3, TRIM29, TYRP1,RBM23, MGP, 8 93/94 EIF1B SPP1, MGP, KRT6B, PPL, RBM23, AQP3, CXCL14,GJA1 8 86/92 BAP1_varB, S100A8, ARG1, S100A9, RBM23, DSC1, TYRP1, 877/94 CST6 BAP1_varA, BTG1, ARG1, GJA1, EIF1B, TACSTD2, TYRP1, 8 74/89LTA4H GJA1, ID2, KRT14, ROBO1, RBM23, TRIM29, LTA4H, S100A9 8 70/69DSC1, SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H, CST6 8 72/86 KRT14,ROBO1, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29, 8 68/72 AQP3 BAP1_varA,MGP, SPP1, CXCL14, BAP1_varB, CLCA2, 8 81/83 S100A8, BTG1 SAP130, ARG1,KRT6B, GJA1, EIF1B, ID2, EIF1B, S100A9, 8 84/81 CRABP2 MGP, SAP130,GJA1, ID2, S100A9, ROBO1, AQP3, LTA4H 8 65/69 SAP130, ARG1, KRT6B,EIF1B, S100A9, KRT14, ROBO1, 12 91/97 RBM23, TRIM29, AQP3, TYRP1, CST6GJA1, PPL, ROBO1, MGP, TRIM29, AQP3, RBM23, 12 93/94 TACSTD2, TYRP1,KRT6B, EIF1B, DSC1 CRABP2, TYRP1, PPL, EIF1B, SPRR1B, DSC1, GJA1, 1293/89 AQP3, MGP, RBM23, CLCA2, TRIM29 RBM23, TACSTD2, CRABP2, PPL, GJA1,SPP1, CXCL14, 12 91/89 EIF1B, AQP3, MGP, LTA4H, KRT6B BAP1_varA, SPP1,BAP1_varB, S100A8, SAP130, KRT6B, ID2, 12 86/83 S100A9, ROBO1, TACSTD2BAP1_varA, MGP, S100A8, BTG1, ARG1, S100A9, KRT14, 12 75/75 ROBO1,SPRR1B, TRIM29, AQP3, LTA4H SPP1, BAP1_varB, CLCA2, SAP130, GJA1,S100A9, RBM23, 12 84/89 SPRR1B, TYRP1, BTG1, KRT6B EIF1B, S100A9,CRABP2, KRT14, ROBO1, RBM23, TRIM29, 12 82/89 AQP3, TYRP1, PPL, LTA4H,CST6 CXCL14, BAP1_varB, CLCA2, S100A8, BTG1, SAP130, 12 79/92 S100A9,CRABP2, KRT14, ROBO1, RMB23, TACSTD2 MGP, CLCA2, S100A8, ARG1, GJA1,ID2, S100A9, ROBO1, 12 70/78 RBM23, SPRR1B, TRIM29, AQP3 S100A8,TACSTD2, BAP1_varA, KRT6B, EIF1B, TRIM29, 14 91/89 TYRP1, CST6, PPL,RBM23, AQP3, GJA1, SPRR1B, ARG1 BAP1_varB, CLCA2, BTG1, SAP130, GJA1,ID2, S100A9, 14 86/89 CRABP2, RBM23, TACSTD2, DSC1, LTA4H, SPP1, KRT6BSPP1, CLCA2, S100A8, SAP130, ARG1, ID2, EIF1B, S100A9, 14 86/97 KRT14,ROBO1, DSC1, TRIM29, TYRP1, LTA4H CXCL14, BAP1_varB, S100A8, BTG1, ARG1,KRT6B, ID2, 14 89/82 EIF1B, CRABP2, KRT14, RBM23, TACSTD2, SPRR1B,TRIM29 BAP1_varA, MGP, SPP1, CXCL14, BAP1_varB, CLCA2, 14 84/86 S100A8,BTG1, SAP130, ARG1, KRT6B, GJA1, ID1, EIF1B S100A9, CRABP2, KRT14,ROBO1, RBM23, TACSTD2, DSC1, 14 82/78 SPRR1B, TRIM29, AQP3, TYRP1, PPL,LTA4H, CST6 BTG1, SAP130, ARG1, GJA1, EIF1B, CRABP2, ROBO1, 14 84/86RBM23, TACSTD2, DSC1, SPRR1B, AQP3, PPL, CST6 BAP1_varA, MGP, BAP1_varB,CLCA2, BTG1, SAP130, GJA1, 14 72/72 ID2, S100A9, CRABP2, RBM23, TACSTD2,DSC1, LTA4H MGP, CXCL14, S100A8, BTG1, ARG1, GJA1, ID2, S100A9, 14 82/86KRT14, ROBO1, TACSTD2, SPRR1B, TRIM29, TYRP1 S100A8, BTG1, SAP130,EIF1B, S100A9, CRABP2, KRT14, 14 79/86 DSC1, SPRR1B, TRIM29, AQP3, CST6,BAP1_varB, LTA4H CST6, KRT6B, LTA4H, CLCA2, CRABP2, TRIM29, 18 91/89CXCL14, PPL, ARG1, RBM23, GJA1, AQP3, TYRP1, SPP1, DSC1, TACSTD2, EIF1B,BAP1_varA MGP, CXCL14, CLCA2, BTG1, ARG1, GJA1, EIF1B, CRABP2, 18 81/92ROBO1, TACSTD2, DSC1, SPRR1B, TRIM29, AQP3, TYRP1, PPL, LTA4H, CST6 MGP,BAP1_varB, CLCA2, S100A8, BTG1, KRT6B, GJA1, ID2, 18  81/100 EIF1B,S100A9, ROBO1, RBM23, TACSTD2, AQP3, TYRP1, PPL, LTA4H, SPRR1B

Example 6 28-Gene Signature for Cutaneous Melanoma Predicts AggressiveTumor Progression to SLN, Distant Lymph Nodes, in Transit Metastasis,Distant Metastasis, and Local Recurrence

A total of 29 stage III, sentinel lymph node (SNL) positive, sampleswere included in the study population (17 in the training set and 12 inthe validation set). These results are shown below in Table 4. RBMprediction of risk for the stage III samples resulted in 25 of 29 (86%)cases classified as high risk, class 2. Metastasis beyond the SLN wasdocumented for 23 of the 29 cases, and 21 of those were predicted to beclass 2 by the algorithm. Metastatic events localized to distant lymphnodes were documents for 13 cases, 12 (92%) of which were accuratelypredicted to be class 2. Similarly, 6 of 7 (86%) of in transitmetastasis cases were assigned to class 2, indicating that the 28-genepredictive signature accurately predicts all aggressive cutaneousmelanoma tumor types, including SLN positive, distant nodal, in transit,distant visceral metastases and local recurrence.

TABLE 4 Accuracy of the CM 28-gene signature for predicting metastaticrisk in SLN, distant lymph nodes, in transit metastasis, and locallyrecurrent disease. training set validation set called called total class2 total class 2 SLN+ (stage III) 17  14 (82%) 12  11 (92%)  samplesdocumented met 12  11 (92%) 11  10 (91%)  event no documented 5  3 (60%)1  1 (100%) met event distant lymph 6 (2 SLN+)  6 (100%) 7 (5 SLN+) 6(86%) node metastasis in transit 3  2 (66%) 4  4 (100%) metastasis localrecurrence 9 4* (44%)  5** 3 (60%) *2/4 LR have known metastatic event(both called class 2) **1/5 LR is stage III (SLN+) - called class 2

Example 7 Statistical Comparison of GEP Signature to Common CMPrognostic Factors

Cox univariate and multivariate regression analysis was performed forthe 107 sample validation cohort following prediction of metastatic risk(Tables 5 and 6). GEP was compared to the individual prognostic factorscomprising AJCC pathologic stage, including Breslow thickness, mitoticrate, and ulceration status, and was also compared directly to AJCCstage. AJCC stage was analyzed as known in the art as Stage IA, IB, IIAvs. IIB, IIC, III, IV for the complete validation set, and as Stage IA,IB, IIA vs. IIB, IIC for analysis that included only Stage I and II.Thus, analysis was performed with inclusion of all validation set cases(Table 5), or with inclusion of only stage I and stage II cases (Table6). Cox univariate analysis resulted in significant correlation withmetastasis for all factors except mitotic rate regardless of whether allvalidation set cases were included, or whether inclusion was restrictedto stage I and II cases only. Comparison of GEP to Breslow thickness,ulceration, and mitotic rate showed that GEP and ulceration weresignificantly correlated to metastatic risk in a multivariate analysis(HR=8.4 and 2.6, p=0.04 and 0.03, respectively). Direct comparison toAJCC stage by multivariate analysis indicates that both factors aresignificantly correlated to metastasis (GEP HR=10.3, p<0.004; AJCCHR=6.4, p<0.0002). These results indicate that the 28-gene signature isable to predict metastasis independently of AJCC TNM staging and otherprognostic factors.

TABLE 5 Univariate and multivariate analysis of cutaneous melanomaprognostic factors with inclusion of cases from all AJCC stages.Univariate Multivariate Variable P-value HR CI P-value HR CI Breslow(0.75 mm) 0.002 24 2.0 0.09 16 3.2 Ulceration <10−7 11.6 0.8 0.03 2.60.9 Mitotic 0.06  4.0 1.4 0.13 3.1 1.4 GEP test <10−6 32 1.4 0.04 8.42.0 AJCC <10−7 20 0.9 0.0002 6.4 1.0 GEP test <10−6 32 1.4 0.004 10.31.6An important clinical application of the GEP test is to identifypatients who are staged as low risk (clinical stage I and II) formetastasis but who are at actual high risk for metastasis based upon thegenomic signature of their CM. The Cox univariate and multivariateregression analysis was performed for those patients in the 107 samplevalidation cohort following prediction of metastatic risk (Table 6) whohad clinical stage I and II CM only. Again, GEP was compared to theelements comprising AJCC pathologic stage: Breslow thickness, mitoticrate, and ulceration status, and directly to AJCC stage. Underunivariate analysis, Breslow's thickness, ulceration status and GEPClass 2 were significant (p<0.0005). However, under multivariateanalysis, only GEP Class 2 was significant (Hazard ratio=27; p=0.01).Univariate analyses for comparison of AJCC stage to GEP Class 2 showedboth to be statistically significant, but the GEP Class 2 variable had agreater Hazard ratio than AJCC stage (48 vs 15, respectively).Multivariate analyses demonstrated that both factors were independent ofeach other (p<0.006) but the Hazard Ratio for the GEP Class 2 was againgreater than that of AJCC stage (23 vs. 4.0). Together, these two resulttables (Table 5 and 6) indicate that the 28-gene signature predictsmetastasis independently of AJCC TNM staging and other prognosticfactors in patients of all stage (I, II, III and IV).

TABLE 6 Univariate and multivariate analysis of cutaneous melanomaprognostic factors with inclusion of cases from AJCC stages I and II.Univariate Multivariate Variable P-value HR CI P-value HR CI Breslow(0.75 mm) 0.0005 151 2.8 0.97 1.1 4.2 Ulceration <10−6 11 1.0 0.22 1.91.0 Mitotic 0.2   2.6 1.5 0.32 2.1 1.4 GEP test 0.0002 48 2.0 0.01 272.5 AJCC <10−7 15 1.0 0.006 4.0 1.0 GEP test 0.0002 48 2.0 0.004 23 2.1

Example 8 28-Gene Signature for Cutaneous Melanoma Predicts Metastasisin Superficial Spreading and Nodular Growth Patterns

Of the four growth patterns described for CM tumors, superficialspreading is the most prevalent, and biologically distinct from nodularmelanomas.¹³ Radial basis machine analysis of the superficial spreadingtumors within the cohort (n=128) resulted in a highly accurateclassification of metastatic and non-metastatic tumors (FIG. 5A). K-MMFS analysis shows that cases predicted to be class 1 have a 100% 5-yearmetastasis free rate, while high risk class 2 cases have only a 5% MFSrate. Additionally, an ROC=1.00 reflects 100% accuracy of the predictionfor superficial spreading tumors. The genetic signature is also able topredict metastatic potential for nodular type CM tumors (FIG. 5B). K-MMFS rate is 81% for class 1 cases, compared to 7% for class 2 cases(p<0.0001). Accuracy of the predicted model was high, as evidenced byROC=0.9323, accuracy of 82%, and sensitivity of 81%. These resultsindicate that the genetic signature has high accuracy for predictingsuperficial spreading disease and lower accuracy for predicting nodulardisease. While the invention has been described in terms of variousembodiments, it is understood that variations and modifications willoccur to those skilled in the art. Therefore, it is intended that theappended claims cover all such equivalent variations that come withinthe scope of the invention as claimed. In addition, the section headingsused herein are for organizational purposes only and are not to beconstrued as limiting the subject matter described.

Each embodiment herein described may be combined with any otherembodiment or embodiments unless clearly indicated to the contrary. Inparticular, any feature or embodiment indicated as being preferred oradvantageous may be combined with any other feature or features orembodiment or embodiments indicated as being preferred or advantageous,unless clearly indicated to the contrary.

Example 9 Trimodal and Linear Methods for Predicting Metastatic RiskAccording to GEP

While a bimodal (low risk Class 1 vs high risk Class 2) may be thepreferred clinical reporting output, additional analysis suggest thatthe tri-modal or linear approach may be clinically appropriate. Analysisusing a trimodal approach demonstrated a stratification outcome relatedto metastatic risk. Specifically, development of a Class A (low risk),Class B (medium risk) and Class C (high risk) trimodal output shows agraduated increase in metastatic risk (FIG. 6). Analysis shows 5-yr MFSfor the independent 107 sample validation set is 98% for class Acompared to 94% for class B and 30% for class C (p<0.0001), with a 64%rate in the overall cohort, reflecting clinically valuable models.

Probability of the metastatic risk prediction is based upon thecoefficients of the 28 variables (genes) and reported as a value between0 and 1. For the two-class analysis, cases with a probability scorebetween 0 and 0.5 are designated low risk, class 1 cases. Conversely,cases with a probability score between 0.5 and 1.0 are classified asclass 2, high risk. The linear output of the probability score can alsobe used directly to assess the risk associated with the geneticsignature of particular CM tumor. As shown in FIG. 7, 5 year MFS ofcases in the 107 sample validation cohort is strongly correlated withthe probability of metastasis. Assessment of all cases with aprobability of metastasis lower than 0.399 shows that none of thosecases has had a documented metastatic event to date, and 5 year MFS is100%. Two of the 17 cases that fall between the probability scores of0.4 and 0.499 have evidence of a metastatic event, and 5 year MFS forthis group is 87%. Those cases with probability scores between 0.5 and1.0 have a significant reduction in 5 year MFS. As illustrated by curvesF-J in FIG. 5, 0.1 increment increases in the probability score lead tocorresponding reductions in 5 year MFS from 56% to 46%, 22%, 16%, and0%. Thus, the utility of the 28 gene predictive signature lies in boththe class assignment of metastatic risk and the linear correlation ofprobability with metastatic outcome.

All references cited in this application are expressly incorporated byreference herein.

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1: A method for predicting the risk of metastasis of a primary cutaneousmelanoma tumor in a patient, the method comprising: (a) obtaining aprimary cutaneous melanoma tumor sample from the patient and isolatingmRNA from the sample; (b) determining the expression levels of at leasteight genes in a gene set by reverse transcribing the isolated mRNA andmeasuring a level of fluorescence for the at least eight genes by anucleic acid sequence detection system following RT-PCR; wherein thegene set comprises the genes BAP1_var1, BAP1_var2, MGP, SPP1, CXCL14,CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B, GJA, ID2, EIF1B, S100A9,CRABP2, KRT14, ROBO1, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29, AQP3, TYRP1,PPL, LTA4H, and CST6; (c) comparing the expression levels of the genesin the gene set from the primary cutaneous melanoma tumor to theexpression levels of the genes in the gene set from a predictivetraining set to generate a probability of metastatic risk predictionvalue; and (d) providing an indication as to whether the primarycutaneous melanoma tumor has a low risk to a high risk of metastasisbased on the probability of metastatic risk prediction value generatedin step (c). 2: The method of claim 1, wherein the cutaneous melanomatumor sample is obtained from a formalin-fixed, paraffin embedded widelocal excision sample. 3: The method of claim 1, wherein the cutaneousmelanoma tumor sample is obtained from formalin-fixed, paraffin embeddedpunch biopsy sample. 4: The method of claim 1, wherein the probabilityof metastatic risk prediction value is between 0 and 1, and wherein avalue of 1 indicates a higher probability of metastasis than a value of0. 5: The method of claim 4, wherein the probability of metastatic riskprediction value is a bimodal, two-class analysis, wherein a patienthaving a value of between 0 and 0.499 is designated as class 1 (lowrisk) and a patient having a value of between 0.500 and 1.00 isdesignated as class 2 (high risk). 6: The method of claim 4, wherein theprobability of metastatic risk prediction value is a tri-modal,three-class analysis, wherein patients are designated as class A (lowrisk), class B (intermediate risk), or class C (high risk). 7: Themethod of claim 1, further comprising identifying the primary cutaneousmelanoma tumor has a high risk of metastasis based on the probability ofmetastatic risk prediction value, and administering to the patient ametastatic cutaneous melanoma tumor treatment. 8: The method of claim 1,wherein the gene set comprises the genes BAP1_var1, BAP1_var2, MGP,SPP1, CXCL14, CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B, GJA, ID2, EIF1B,S100A9, CRABP2, KRT14, ROBO1, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29,AQP3, TYRP1, PPL, LTA4H, and CST6. 9: A method for treating a metastaticcutaneous melanoma tumor in a patient comprising: (a) obtaining adiagnosis identifying a risk of metastasis for a primary cutaneousmelanoma tumor from the patient, wherein the diagnosis was obtained by:(1) determining the expression levels of at least eight genes in a geneset in a primary cutaneous melanoma tumor sample from the patient bymeasuring a level of fluorescence for the at least eight genes by anucleic acid sequence detection system following RT-PCR, wherein thegene set comprises the genes BAP1_var1, BAP1_var2, MGP, SPP1, CXCL14,CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B, GJA, ID2, EIF1B, S100A9,CRABP2, KRT14, ROBO1, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29, AQP3, TYRP1,PPL, LTA4H, and CST6; (2) comparing the expression levels of the genesin the gene set from the primary cutaneous melanoma tumor to theexpression levels of the genes in the gene set from a predictivetraining set to generate a probability of metastatic risk predictionvalue; (3) providing an indication as to whether the primary cutaneousmelanoma tumor has a low risk to a high risk of metastasis based on theprobability of metastatic risk prediction value generated in step (2);and (4) identifying that the primary cutaneous melanoma tumor has a highrisk of metastasis based on the probability of metastatic riskprediction value and diagnosing the tumor as having a high risk ofmetastasis; (b) administering to the patient a metastatic cutaneousmelanoma tumor treatment when the determination is made in theaffirmative that the patient has a primary cutaneous melanoma tumor witha high risk of metastasis. 10: The method of claim 9, further comprisingperforming a sentinel lymph node biopsy (SLNB) on the patient when thedetermination is made in the affirmative that the patient has a primarycutaneous melanoma tumor with a high risk of metastasis. 11: The methodof claim 9, wherein the cutaneous melanoma tumor sample is obtained froma formalin-fixed, paraffin embedded wide local excision sample. 12: Themethod of claim 9, wherein the cutaneous melanoma tumor sample isobtained from a formalin-fixed, paraffin embedded punch biopsy sample.13: The method of claim 9, wherein the probability of metastatic riskprediction value is between 0 and 1, and wherein a value of 1 indicatesa higher probability of metastasis than a value of
 0. 14: The method ofclaim 13, wherein the probability of metastatic risk prediction value isa bimodal, two-class analysis, wherein a patient having a value ofbetween 0 and 0.499 is designated as class 1 (low risk) and a patienthaving a value of between 0.500 and 1.00 is designated as class 2 (highrisk). 15: The method of claim 1, wherein the probability of metastaticrisk prediction value is a tri-modal, three-class analysis, whereinpatients are designated as class A (low risk), class B (intermediaterisk), or class C (high risk). 16: The method of claim 9, wherein thegene set comprises the genes BAP1_var1, BAP1_var2, MGP, SPP1, CXCL14,CLCA2, S100A8, BTG1, SAP130, ARG1, KRT6B, GJA, ID2, EIF1B, S100A9,CRABP2, KRT14, ROBO1, RBM23, TACSTD2, DSC1, SPRR1B, TRIM29, AQP3, TYRP1,PPL, LTA4H, and CST6.